code: def _var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
      arr = asanyarray(a)
  
      rcount = _count_reduce_items(arr, axis)
      # Make this warning show up on top.
      if ddof >= rcount:
          warnings.warn(
              "Degrees of freedom <= 0 for slice",
              RuntimeWarning,
              stacklevel=2,
          )
  
      # Cast bool, unsigned int, and int to float64 by default
      if dtype is None and issubclass(
          arr.dtype.type, (nt.integer, nt.bool_)
      ):
          dtype = mu.dtype("f8")
  
      # Compute the mean.
      # Note that if dtype is not of inexact type then arraymean will
      # not be either.
      arrmean = umr_sum(arr, axis, dtype, keepdims=True)
      if isinstance(arrmean, mu.ndarray):
          arrmean = um.true_divide(
              arrmean,
              rcount,
              out=arrmean,
              casting="unsafe",
              subok=False,
          )
      else:
          arrmean = arrmean.dtype.type(arrmean / rcount)
  
      # Compute sum of squared deviations from mean
      # Note that x may not be inexact and that we need it to be an array,
      # not a scalar.
      x = asanyarray(arr - arrmean)
      if issubclass(arr.dtype.type, nt.complexfloating):
          x = um.multiply(x, um.conjugate(x), out=x).real
      else:
          x = um.multiply(x, x, out=x)
      ret = umr_sum(x, axis, dtype, out, keepdims)
  
      # Compute degrees of freedom and make sure it is not negative.
      rcount = max([rcount - ddof, 0])
  
      # divide by degrees of freedom
      if isinstance(ret, mu.ndarray):
          ret = um.true_divide(
              ret, rcount, out=ret, casting="unsafe", subok=False
          )
      elif hasattr(ret, "dtype"):
          ret = ret.dtype.type(ret / rcount)
      else:
          ret = ret / rcount
  
      return ret
  
file_path: catboost/contrib/python/numpy/py2/numpy/core/_methods.py
imports: from __future__ import division, absolute_import, print_function
  import warnings
  from numpy.core import multiarray
  from numpy.core import umath
  from numpy.core.numeric import asanyarray
  from numpy.core import numerictypes
  from numpy._globals import _NoValue
label_desc: Use image node_id 9 for calling a global function with example usage: _var(a, axis, dtype, out, ddof, keepdims) and returns: ret